Research On Task Scheduling Algorithm Based On Cloud Computing

As a business model in the IT field that provides convenient,fast,and on-demand network access,cloud computing is composed of many technologies.Task scheduling is the key technology to control resources and improve system stability,which directly affects the user experience.So far,many algorithm designs for task scheduling have become a hot topic.Effective combination in the existing task scheduling algorithm can save the task completion time,meet the user's service quality requirements and improve the system load balancing.The paper comprehensively analyzes and studies the task scheduling algorithm in the cloud environment to solve the problems of long execution time,heavy system load and low user service quality experience of a single scheduling algorithm.The main contents and work of the thesis are as follows:(1)Firstly,the related theories of cloud computing are expounded,and the task scheduling process,virtual machine scheduling model,characteristics and scheduling goals are introduced.(2)Secondly,by combining the complex problems in the existing scheduling algorithm,a particle swarm ant colony algorithm is proposed.The algorithm is aimed at the early convergence of the particle swarm optimization algorithm and the lack of the global optimal solution.The ant colony algorithm stably searches for the characteristics of the global optimal solution throughout the process.Firstly,the particle cluster optimization algorithm is used to quickly find the current optimal solution of the particle,and then the ant colony algorithm is used to find the global optimal solution.The simulation proves that the algorithm can effectively save search time and improve the efficiency of the algorithm.(3)Finally,in order to further optimize the algorithm,the method of randomly searching for resources from the ant colony algorithm part and the incremental fixing of pheromone are used to integrate the Min-Min algorithm into the ant colony part,and improve the quality of random search.The probability of resources,and the pheromone update rules are dynamically adjusted to improve the task execution time,user service quality and load balancing of the algorithm in the cloud environment.The algorithm is verified by simulation.The simulation results show that the particle swarm ant colony algorithm integrated with Min-Min algorithm has higher performance than other scheduling algorithms.